




"""simple docstring"""





import glob
import os
import random
from string import ascii_lowercase, digits

import cva
A				=							""""""
A				=							""""""
A				=							""""""
A				=							1  # (0 is vertical, 1 is horizontal)

def        _UpperCamelCase     (      )      ->      None:


			"""simple docstring"""
			__UpperCAmelCase						,  __UpperCAmelCase						:			Optional[Any]												=       get_dataset(UpperCamelCase					,							UpperCamelCase				)
			print("Processing..."				)
			__UpperCAmelCase						,  __UpperCAmelCase						,  __UpperCAmelCase						:			List[Any]												=       update_image_and_anno(UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase				)

			for index, image in enumerate(UpperCamelCase				):
						# Get random string code: '7b7ad245cdff75241935e4dd860f3bad'
						__UpperCAmelCase						:			List[Any]												=       random_chars(32				)
						__UpperCAmelCase						:			List[str]												=       paths[index].split(os.sep				)[-1].rsplit("."					,							1				)[0]
						__UpperCAmelCase						:			str												=       f"{OUTPUT_DIR}/{file_name}_FLIP_{letter_code}"
						cva.imwrite(f"/{file_root}.jpg"					,							UpperCamelCase					,							[cva.IMWRITE_JPEG_QUALITY, 85]				)
						print(f"Success {index+1}/{len(UpperCamelCase				)} with {file_name}"				)
						__UpperCAmelCase						:			Dict												=       []
						for anno in new_annos[index]:
									__UpperCAmelCase						:			Any												=       f"{anno[0]} {anno[1]} {anno[2]} {anno[3]} {anno[4]}"
									annos_list.append(UpperCamelCase				)
						with open(f"/{file_root}.txt"					,							"w"				) as outfile:
									outfile.write("\n".join(line for line in annos_list				)				)

def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase				)      ->      tuple[list, list]:


			"""simple docstring"""
			__UpperCAmelCase						:			Optional[Any]												=       []
			__UpperCAmelCase						:			Optional[int]												=       []
			for label_file in glob.glob(os.path.join(UpperCamelCase					,							"*.txt"				)				):
						__UpperCAmelCase						:			str												=       label_file.split(os.sep				)[-1].rsplit("."					,							1				)[0]
						with open(UpperCamelCase				) as in_file:
									__UpperCAmelCase						:			int												=       in_file.readlines()
						__UpperCAmelCase						:			Dict												=       os.path.join(UpperCamelCase					,							f"{label_name}.jpg"				)

						__UpperCAmelCase						:			int												=       []
						for obj_list in obj_lists:
									__UpperCAmelCase						:			Union[str, Any]												=       obj_list.rstrip("\n"				).split(" "				)
									boxes.append(
									    [
									        int(obj[0]				),
									        float(obj[1]				),
									        float(obj[2]				),
									        float(obj[3]				),
									        float(obj[4]				),
									    ]				)
						if not boxes:
									continue
						img_paths.append(UpperCamelCase				)
						labels.append(UpperCamelCase				)
			return img_paths, labels

def        _UpperCamelCase     (      UpperCamelCase					,							UpperCamelCase					,							UpperCamelCase = 1				)      ->      tuple[list, list, list]:


			"""simple docstring"""
			__UpperCAmelCase						:			Dict												=       []
			__UpperCAmelCase						:			Tuple												=       []
			__UpperCAmelCase						:			Optional[Any]												=       []
			for idx in range(len(UpperCamelCase				)				):
						__UpperCAmelCase						:			Tuple												=       []
						__UpperCAmelCase						:			Optional[Any]												=       img_list[idx]
						path_list.append(UpperCamelCase				)
						__UpperCAmelCase						:			Union[str, Any]												=       anno_list[idx]
						__UpperCAmelCase						:			List[str]												=       cva.imread(UpperCamelCase				)
						if flip_type == 1:
									__UpperCAmelCase						:			List[str]												=       cva.flip(UpperCamelCase					,							UpperCamelCase				)
									for bbox in img_annos:
												__UpperCAmelCase						:			int												=       1 - bbox[1]
												new_annos.append([bbox[0], x_center_new, bbox[2], bbox[3], bbox[4]]				)
						elif flip_type == 0:
									__UpperCAmelCase						:			List[str]												=       cva.flip(UpperCamelCase					,							UpperCamelCase				)
									for bbox in img_annos:
												__UpperCAmelCase						:			Optional[int]												=       1 - bbox[2]
												new_annos.append([bbox[0], bbox[1], y_center_new, bbox[3], bbox[4]]				)
						new_annos_lists.append(UpperCamelCase				)
						new_imgs_list.append(UpperCamelCase				)
			return new_imgs_list, new_annos_lists, path_list

def        _UpperCamelCase     (      UpperCamelCase = 32				)      ->      str:


			"""simple docstring"""
			assert number_char > 1, "The number of character should greater than 1"
			__UpperCAmelCase						:			List[str]												=       ascii_lowercase + digits
			return "".join(random.choice(UpperCamelCase				) for _ in range(UpperCamelCase				)				)


if __name__ == "__main__":
					main()
					print("""DONE ✅""")







'''simple docstring'''






from typing import List, Optional, Union

from ...processing_utils import ProcessorMixin
from ...tokenization_utils_base import BatchEncoding, PaddingStrategy, PreTokenizedInput, TextInput, TruncationStrategy
from ...utils import TensorType

class 				a__(   lowerCamelCase__      ):
    lowercase__					=       ["""image_processor""", """tokenizer"""]
    lowercase__					=       """BridgeTowerImageProcessor"""
    lowercase__					=       ("""RobertaTokenizer""", """RobertaTokenizerFast""")







    def __init__( self					:    int		,     __snake_case					:    str		,     __snake_case					:    List[str]      ):
      super().__init__(__snake_case		,     __snake_case      )







    def __call__( self					:    int		,     __snake_case					:    Optional[Any]		,     __snake_case					:    Union[TextInput, PreTokenizedInput, List[TextInput], List[PreTokenizedInput]] = None		,     __snake_case					:    bool = True		,     __snake_case					:    Union[bool, str, PaddingStrategy] = False		,     __snake_case					:    Union[bool, str, TruncationStrategy] = None		,     __snake_case					:    Optional[int] = None		,     __snake_case					:    int = 0		,     __snake_case					:    Optional[int] = None		,     __snake_case					:    Optional[bool] = None		,     __snake_case					:    Optional[bool] = None		,     __snake_case					:    bool = False		,     __snake_case					:    bool = False		,     __snake_case					:    bool = False		,     __snake_case					:    bool = False		,     __snake_case					:    bool = True		,     __snake_case					:    Optional[Union[str, TensorType]] = None		,     **__snake_case					:    List[Any]		,     ):
      a		:   Optional[int]	  =	self.tokenizer(
          text=__snake_case		,     add_special_tokens=__snake_case		,     padding=__snake_case		,     truncation=__snake_case		,     max_length=__snake_case		,     stride=__snake_case		,     pad_to_multiple_of=__snake_case		,     return_token_type_ids=__snake_case		,     return_attention_mask=__snake_case		,     return_overflowing_tokens=__snake_case		,     return_special_tokens_mask=__snake_case		,     return_offsets_mapping=__snake_case		,     return_length=__snake_case		,     verbose=__snake_case		,     return_tensors=__snake_case		,     **__snake_case		,     )
      # add pixel_values + pixel_mask
      a		:   List[str]	  =	self.image_processor(
          __snake_case		,     return_tensors=__snake_case		,     do_normalize=__snake_case		,     do_center_crop=__snake_case		,     **__snake_case      )
      encoding.update(__snake_case      )

      return encoding







    def      lowercase_			( self					:    int		,     *__snake_case					:    List[str]		,     **__snake_case					:    List[str]      ):
      return self.tokenizer.batch_decode(*__snake_case		,     **__snake_case      )







    def      lowercase_			( self					:    List[str]		,     *__snake_case					:    Tuple		,     **__snake_case					:    Union[str, Any]      ):
      return self.tokenizer.decode(*__snake_case		,     **__snake_case      )







    @property
    def      lowercase_			( self					:    Optional[Any]      ):
      a		:   Optional[Any]	  =	self.tokenizer.model_input_names
      a		:   Union[str, Any]	  =	self.image_processor.model_input_names
      return list(dict.fromkeys(tokenizer_input_names + image_processor_input_names      )      )